Related references
Note: Only part of the references are listed.Identification of tea white star disease and anthrax based on hyperspectral image information
Bing Lu et al.
JOURNAL OF FOOD PROCESS ENGINEERING (2021)
Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis
Huiqin Ma et al.
SENSORS (2020)
Diagnosis of the Severity of Fusarium Head Blight of Wheat Ears on the Basis of Image and Spectral Feature Fusion
Linsheng Huang et al.
SENSORS (2020)
Identification of Wheat Yellow Rust Using Spectral and Texture Features of Hyperspectral Images
Anting Guo et al.
REMOTE SENSING (2020)
Integrating spectral and image data to detect Fusarium head blight of wheat
Dong-Yan Zhang et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)
Assessment of rice leaf blast severity using hyperspectral imaging during late vegetative growth
GuoSheng Zhang et al.
AUSTRALASIAN PLANT PATHOLOGY (2020)
Hyperspectral imaging of symptoms induced byRhizoctonia solaniin sugar beet: comparison of input data and different machine learning algorithms
Abel Barreto et al.
JOURNAL OF PLANT DISEASES AND PROTECTION (2020)
Detecting powdery mildew disease in squash at different stages using UAV-based hyperspectral imaging and artificial intelligence
Jaafar Abdulridha et al.
BIOSYSTEMS ENGINEERING (2020)
Integration of spectroscopy and image for identifying fusarium damage in wheat kernels
Dongyan Zhang et al.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY (2020)
Early detection of black Sigatoka in banana leaves using hyperspectral images
Jorge Ugarte Fajardo et al.
APPLICATIONS IN PLANT SCIENCES (2020)
Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods
Lei Feng et al.
FRONTIERS IN PLANT SCIENCE (2020)
Statistical analysis of phenotypic traits of rice (Oryza sativa L.) related to grain yield under neck blast disease
Seyedeh Soheila Zarbafi et al.
JOURNAL OF PLANT DISEASES AND PROTECTION (2019)
A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images
Xin Zhang et al.
REMOTE SENSING (2019)
Plant disease identification using explainable 3D deep learning on hyperspectral images
Koushik Nagasubramanian et al.
PLANT METHODS (2019)
Rice Blast: A Disease with Implications for Global Food Security
Aziiba Emmanuel Asibi et al.
AGRONOMY-BASEL (2019)
Predicting rice blast disease: machine learning versus process-based models
David F. Nettleton et al.
BMC BIOINFORMATICS (2019)
Monitoring plant diseases and pests through remote sensing technology: A review
Jingcheng Zhang et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)
Evaluation of image processing technique in identifying rice blast disease in field conditions based on KNN algorithm improvement by K-means
Mohammad Reza Larijani et al.
FOOD SCIENCE & NUTRITION (2019)
Research on optimal predicting model for the grading detection of rice blast
Ya-hui Luo et al.
OPTICAL REVIEW (2019)
Detection of Fire Blight disease in pear trees by hyperspectral data
Nikrooz Bagheri et al.
EUROPEAN JOURNAL OF REMOTE SENSING (2018)
A deep learning framework to discern and count microscopic nematode eggs
Adedotun Akintayo et al.
SCIENTIFIC REPORTS (2018)
Using Image Texture and Spectral Reflectance Analysis to Detect Yellowness and Esca in Grapevines at Leaf-Level
Hania Al-Saddik et al.
REMOTE SENSING (2018)
Increase in crop losses to insect pests in a warming climate
Curtis A. Deutsch et al.
SCIENCE (2018)
Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems
Koushik Nagasubramanian et al.
PLANT METHODS (2018)
Spatial Referencing of Hyperspectral Images for Tracing of Plant Disease Symptoms
Jan Behmann et al.
JOURNAL OF IMAGING (2018)
Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images
Uwe Knauer et al.
PLANT METHODS (2017)
A real-time phenotyping framework using machine learning for plant stress severity rating in soybean
Hsiang Sing Naik et al.
PLANT METHODS (2017)
Hyperspectral Imaging for Presymptomatic Detection of Tobacco Disease with Successive Projections Algorithm and Machine-learning Classifiers
Hongyan Zhu et al.
SCIENTIFIC REPORTS (2017)
Current Status of Conventional and Molecular Interventions for Blast Resistance in Rice
Deepti Srivastava et al.
RICE SCIENCE (2017)
Computer vision and machine learning for robust phenotyping in genome-wide studies
Jiaoping Zhang et al.
SCIENTIFIC REPORTS (2017)
Rotation-Based Support Vector Machine Ensemble in Classification of Hyperspectral Data With Limited Training Samples
Junshi Xia et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2016)
Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks
Yushi Chen et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2016)
Random frog: An efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification
Hong-Dong Li et al.
ANALYTICA CHIMICA ACTA (2012)
Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis
Zhan-Yu Liu et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2010)
Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging
C. H. Bock et al.
CRITICAL REVIEWS IN PLANT SCIENCES (2010)
Hyperspectral imaging - an emerging process analytical tool for food quality and safety control
A. A. Gowen et al.
TRENDS IN FOOD SCIENCE & TECHNOLOGY (2007)
Extreme learning machine: Theory and applications
Guang-Bin Huang et al.
NEUROCOMPUTING (2006)
The successive projections algorithm for variable selection in spectroscopic multicomponent analysis
MCU Araújo et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2001)
Share Paper
